Colorization
The following is a high level project pipeline of my Computational Photography Colorization report. The project scope involves minimizing a quadratic cost function. An artist would only need to make a few colour scribble on a grey photograph and the algorithm will automatically populate the entire photograph with the associated colours.
1.Input: I first read in the image using imread function.
2.Find the difference: Next I compute the difference between the marked and grey scale image. This would feed into step 5.
3.Transform to YIQ space: Then I convert the grey image and the marked version from RGB space to YIQ space 2 & 3 . I wrote a function, rgbToyiq in color_space.py to convert rgb dimension to that of YIQ.
4.Compute weight matrix:
- The next step, also the most complicated one is to compute the weight matrix.
- I first initialize 3 matrices of size height X width X size of window (9): row indices (i, j count), colIndices and values (weights) to hold key information during the loop
- The algo will loop through each pixel. And it will compute the weights (using marked) according to formula below in a window of size 9 i.e. 9 pixels in a window (including the pixel).
5.Solve Ax = B: Once the weights are obtained, I proceed to obtain a least square solution.
6.Lastly, I transform the YIQ output back to RGB space.
Here are the photographs.
